A new denoising method for fMRI based on weighted three-dimensional wavelet transform

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dc.contributor.authorOzmen, Guzin
dc.contributor.authorOzsen, Seral
dc.date.accessioned2020-03-26T19:52:43Z
dc.date.available2020-03-26T19:52:43Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study presents a new three-dimensional discrete wavelet transform (3D-DWT)-based denoising method for functional magnetic resonance images (fMRI). This method is called weighted three-dimensional discrete wavelet transform (w-3D-DWT), and it is based on the principle of weighting the volume subbands which are obtained by 3D-DWT. Briefly, classical DWT denoising consists of wavelet decomposition, thresholding, and image reconstruction steps. In the thresholding algorithm, the thresholding value for each image cannot be chosen exclusively. Namely, a specific thresholding value is chosen and it is used for all images. The proposed algorithm in this study can be considered as a data-driven denoising model for fMRI. It consists of three-dimensional wavelet decomposition, subband weighting, and image reconstruction. The purposes of subband weighting algorithm are to increase the effect of the subband which represents the image better and to decrease the effect of the subband which represents the image in the worst way and thus to reduce the noises of the image adaptively. fMRI is one of the popular methods used to understand brain functions which are often corrupted by noises from various sources. The traditional denoising method used in fMRI is smoothing images with a Gaussian kernel. This study suggests an adaptive approach for fMRI filtering different from Gaussian smoothing and 3D-DWT thresholding. In this study, w-3D-DWT denoising results were evaluated with mean-square error (MSE), peak signal/noise ratio (PSNR), and structural similarity (SSIM) metrics, and the results were compared with Gaussian smoothing and 3D-DWT thresholding methods. According to this comparison, w-3D-DWT gave low-MSE and high-PSNR results for fMRI data.en_US
dc.identifier.doi10.1007/s00521-017-2995-7en_US
dc.identifier.endpage276en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage263en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-017-2995-7
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36258
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900019en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subject3D-DWTen_US
dc.subjectfMRIen_US
dc.subjectGaussian smoothingen_US
dc.subjectWeighting subbandsen_US
dc.titleA new denoising method for fMRI based on weighted three-dimensional wavelet transformen_US
dc.typeArticleen_US

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